hi this is debrupa nandi today is my second day with git
studies
observational - 1. collect data in a way that does not directly interfere with how the data arise(“observe”) 2. only establish an association 3. retrospective uses past data 4. prospective - data are collected throughout the study
experiment- randomly assign subjects to treatments
Confounding variables - extraneous variables that affect both the explanatory and the response variable and that make it seem like there is a relationship between them
Correlation does not imply causation Observational staements help make us make correlation statements Experiments help us infer causation









In statistics, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.




library(dplyr)
library(ggplot2)
library(statsr)
data(arbuthnot)
View(arbuthnot)

library(dplyr)
library(ggplot2)
library(statsr)
data(present)
View(present)
present <- present %>%
mutate(total = boys + girls)
prop_boys <- present$boys/present$total
present <- present %>%
mutate(prop_boys)
ggplot(data = present, aes(x = year, y = prop_boys)) +
geom_line() + geom_point() + geom_smooth()

present <- present %>%
mutate(more_boys = present$boys > present$girls)
View(present)
present <- present %>%
mutate(prop_boy_girl = boys / girls)
View(present)
ggplot(data = present, aes(x = year, y = prop_boy_girl)) +
geom_line() + geom_point() + geom_smooth()

present <- present %>%
arrange(desc(total))
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